Publications by authors named "B Zanutto"
Article Synopsis
- The goal of this research in neuroscience is to understand how neural populations perform computations that enable cognitive skills in animals, using neural network models for developing testable hypotheses.
- The proposed method, called generalised Firing-to-Parameter (gFTP), allows researchers to create binary recurrent neural networks that follow a user-defined transition graph, which outlines how population firing states change in response to stimuli.
- gFTP not only ensures the transition graph is realizable as a neural network but also identifies necessary adjustments while preserving the original graph’s information, allowing for the exploration of the connections between neural structure, function, and computational algorithms.
Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not well characterized.
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- Neural networks help us understand brain function by connecting cellular and circuit levels to behavior.
- The fitting process for neural networks is typically done using optimization algorithms, but this study proposes to reverse that process by analyzing network dynamics to derive network parameters.
- The method was applied to a sequence memory task, demonstrating that the resulting neural networks showed patterns consistent with experimental data, suggesting a new approach to modeling brain function.
Serotonin (5-HT) neurotransmission has been associated with reward-related behaviour. Moreover, the serotonergic system modulates the basolateral amygdala (BLA), a structure involved in reward encoding, and reward prediction error. However, the role played by 5-HT on BLA during a reward-driven task has not been fully elucidated.
View Article and Find Full Text PDFA general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue.
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